System and method for personalized information retrieval based on user expertise

ABSTRACT

A search request is received at an information retrieval system from a searcher. The search request preferably contains at least one search term and a user identifier. A plurality of objects are then searched based on the at least one search term. At least one located object is found from the plurality of objects. The at least one located object is associated with the search term(s). An intrinsic score based on the search term(s) is subsequently calculated for each located object. The intrinsic score is then adjusted to an adjusted score based on the difference between a creator expertise of a creator of the at least one located object and/or a contributor expertise of a contributor to the at least one located object, and a searcher expertise of the searcher.

This is a continuation application of U.S. patent Ser. No. 10/172,165filed Jun. 14, 2002, U.S. Pat. No. 6,892,198, issued May 10, 2005entitled SYSTEM AND METHOD FOR PERSONALIZED INFORMATION RETRIEVAL BASEDON USER EXPERTISE, hereby incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to information retrieval, andmore particularly to a system and method for adjusting search resultsbased on the relative expertise between a searcher and the creator/sand/or contributor/s of a document.

2. Description of Related Art

With the proliferation of corporate networks and the Internet, an everincreasing amount of information is being made available in electronicform. Such information includes documents, graphics, video, audio, orthe like. While corporate information is typically well indexed andstored on corporate databases within a corporate network, information onthe Internet is generally highly disorganized.

Searchers looking for information typically make use of an informationretrieval system. In corporate networks, such an information retrievalsystem typically consists of document management software, such asApplicant's QUANTUM™ suite, or iManage Inc's INFORITE™ or WORKSITE™products. Information retrieval from the internet, however, is typicallyundertaken using a search engine, such as YAHOO™ or GOOGLE™.

Generally speaking, these information retrieval systems extract keywordsfrom each document in a network. Such keywords typically contain nosemantic or syntactic information. For each document, each keyword isthen indexed into a searchable data structure with a link back to thedocument itself. To search the network, a user supplies the informationretrieval system with a query containing one or more search terms, whichmay be separated by Boolean operators, such as “AND” or “OR.” Thesesearch terms can be further expanded through the use of a Thesaurus. Inresponse to the query, which might have been expanded, the informationretrieval system attempts to locate information, such as documents, thatmatch the searcher supplied (or expanded) keywords. In doing so, theinformation retrieval system searches through its databases to locatedocuments that contain at least one keyword that matches one of thesearch terms in the query (or its expanded version). The informationretrieval system then presents the searcher with a list of documentrecords for the documents located. The list is typically sorted based ondocument ranking, where each document is ranked according to the numberof keyword to search term matches in that document relative to those forthe other located documents. An example of a search engine that usessuch a technique, where document relevancy is based solely on thecontent of the document, is INTELISEEK™. However, most documentsretrieved in response to such a query have been found to be irrelevant.

In an attempt to improve precision, a number of advanced informationretrieval techniques have been developed. These techniques includesyntactic processing, natural language processing, semantic processing,or the like. Details of such techniques can be found in U.S. Pat. Nos.5,933,822; 6,182,068; 6,311,194; and 6,199,067, all of which areincorporated herein by reference.

However, even these advanced information retrieval techniques have notbeen able to reach the level of precision required by today'scorporations. In fact, a recent survey found that forty four percent ofusers say that they are frustrated with search engine results. SeeInternet Usage High, Satisfaction low: Web Navigation Frustrate ManyConsumers, Berrier Associates—sponsored by Realnames Corporation (April2000).

In addition, other advanced techniques have also proven to lack adequateprecision. For example, GOOGLE™ and WISENUT™ rank document relevancy asa function of a network of links pointing to the document, while methodsbased on Salton's work (such as ORACLE™ text) rank document relevancy asa function of the number of relevant documents within the repository.

This lack of precision is at least partially caused by currentinformation retrieval systems not taking the personal profiles of thedocument creator, searcher, and any contributors into account. In otherwords, when trying to assess the relevancy of documents within anetwork, most information retrieval systems ignore the searcher thatperforms the query, i.e., most information retrieval systems adopt aone-fit-all approach. For example, when a neurologist and a high schoolstudent both perform a search for “brain AND scan,” an identical list oflocated documents is presented to both the neurologist and the highschool student. However, the neurologist is interested in high leveldocuments containing detailed descriptions of brain scanning techniques,while the student is only interested in basic information on brain scansfor a school project. As can be seen, a document query that does nottake the searcher into account can retrieve irrelevant and impreciseresults.

Moreover, not only should the profession of a searcher affect a searchresult, but also the expertise of the searcher within the search domain.For example, a medical doctor that is a recognized world expert wouldcertainly assign different relevancy scores to the returned documentsthan say an intern. This means that information retrieval systems shouldbe highly dynamic and consider the current expertise level of thesearcher and/or creator/s at the time of the query.

In addition, the current lack of precision is at least partially causedby the treatment of documents as static entities. Current informationretrieval techniques typically do not take into account the dynamicnature of documents. For example, after creation, documents may becommented on, printed, viewed, copied, etc. To this end, documentrelevancy should consider the activity around a document.

Therefore, a need exists in the art for a system and method forretrieving information that can yield a significant improvement inprecision over that attainable through conventional informationretrieval systems. Moreover, such a system and method should preferablypersonalize information retrieval based on user expertise.

BRIEF SUMMARY OF THE INVENTION

According to the invention there is provided a method for personalizinginformation retrieval. A search request is received at an informationretrieval system from a searcher. The search request preferably containsat least one search term and a user identifier. A plurality of objectsare then searched based on the search term(s). Objects preferablyinclude: content objects, such as documents, comments, or folders;source objects; people objects, such as experts, peers, or workgroups;or the like. At least one located object is found from the plurality ofobjects. Each located object is associated with the search term(s). Anintrinsic score based on the search term(s) is subsequently calculatedfor each located object. The intrinsic score is based on the searchterm(s). The intrinsic score is then adjusted to an adjusted score basedon the difference between a creator expertise of the creator of thelocated object and/or a contributor expertise of a contributor/s to thelocated object, and a searcher expertise of the searcher.

To make this adjustment, objects associated with the searcher arelocated using the unique user identifier. The searcher expertise is thenascertained based on the search terms and the objects associated withthe searcher. The creator expertise and/or the contributor/s expertiseis determined for each located object. The intrinsic score, with theexception of people related searches, is then raised to the adjustedscore for each located object having a creator expertise higher than thesearcher expertise. Alternatively, the intrinsic score, with theexception of people related searches, is then lowered to the adjustedscore for each located object having a creator expertise that is lowerthan the searcher expertise. For people related searches, creatorexpertise significantly above or below the searcher expertise negativelyaffect the intrinsic score of the located object. A list is thentransmitted to the searcher to be displayed, where the list is based onthe search request and the adjusted scores.

Accordingly, at the time of the query, the expertise of the searcher istaken into consideration in relation to the expertise of both thecreator/s and contributor/s. Therefore, the present invention yields asignificant improvement in precision over that attainable throughconventional information retrieval systems.

BRIEF DESCRIPTION OF THE DRAWINGS

Additional features of the invention will be more readily apparent fromthe following detailed description and appended claims when taken inconjunction with the drawings, in which:

FIG. 1 is a block diagram of a system architecture for a system forpersonalizing information retrieval;

FIG. 2 is a block diagram of a creator device, contributor device, orsearcher device, as shown in FIG. 1;

FIG. 3 is a block diagram of the information retrieval system andRepository of FIG. 1;

FIG. 4 is a flow chart of document collection according to an embodimentof the invention; and

FIG. 5 is a flow chart of a process for information retrieval accordingto an embodiment of the invention.

Like reference numerals refer to corresponding parts throughout theseveral views of the drawings. For ease of reference, the first number/sof any reference numeral generally indicates the number of the figurewhere the reference numeral can be found. For example, 112 can be foundon FIG. 1, and 324 can be found on FIG. 3.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a block diagram of a system architecture 100 for a system forpersonalizing information retrieval. An information retrieval system 102is coupled to a repository 104 and to a network 110. Also coupled to thenetwork 110 are a searcher device 108, one or more creator device/s 106,and one or more contributor device/s 112. Searcher device 108, creatordevice/s 106, contributor device/s 112, and information retrieval system102 are all computing devices, such as clients, servers, or the like.The network is preferably a Local Area Network (LAN), but alternativelymay be any network, such as the Internet. It should be appreciated thatalthough searcher device 108, creator device/s 106, contributor device/s112, and information retrieval system 102 are shown as distinctentities, they may be combined into one or more devices. Further detailsof the searcher device 108, creator device/s 106, contributor device/s112, and information retrieval system 102 can be found below in relationto FIGS. 2–5.

The repository 104 is any storage device/s that is capable of storingdata, such as a hard disk drive, magnetic media drive, or the like. Therepository 104 is preferably contained within the information retrievalsystem 102, but is shown as a separate compnent for ease ofexplaination. Alternatively, the repository 104 may be dispersedthroughout a network, and may even be located within the searcher device108, creator device/s 106, and/or contributor device/s 112.

Each creator device 106 is a computing device operated by a creator whocreates one or more documents. Each contributor device 112 is acomputing device operated by a contributor who contributes to a documentby, for example, adding to, commenting on, viewing, or otherwiseaccessing documents created by a creators. The searcher device 108 is acomputing device operated by a searcher who is conducting a search forrelevant documents created by the creator/s or contributed to by thecontributor/s. The searcher, creator/s, and contributor/s are notlimited to the above described roles and may take on any role atdifferent times. Also, the searcher, creator/s, and contributor/s maybrowse the repository 104 without the use of the information retrievalsystem 102.

FIG. 2 is a block diagram of a creator device 106, contributor device112, or searcher device 108, as shown in FIG. 1. The devices 106/108/112preferably include the following cmponents: at least one data processoror central processing unit (CPU) 202; a memory 214; input and/or outputdevices 206, such as a monitor and keyboard; communications circuitry204 for communicating with the network 110 (FIG. 1) and informationretrieval system 102 (FIG. 1); and at least one bus 210 thatinterconnects these components.

Memory 214 preferably includes an operating system 216, such as but notlimited to, VXWORKS™, LINUX™, or WINDOWS™ having instructions forprocessing, accessing, storing, or searching data, etc. Memory 214 alsopreferably includes communication procedures for communicating with thenetwork 110 (FIG. 1) and information retrieval system 102 (FIG. 1);searching procedures 220, such as proprietary search software, aWeb-browser, or the like; application programs 222, such as a wordprocessor, email client, database, or the like; a unique user identifier224; and a cache 226 for temporarily storing data. The unique useridentifier 224 may be supplied by the creator/searcher/contributor eachtime he or she performs a search, such as by supplying a username.Alternatively, the unique user identifier 224 may be the user's loginusername, Media Access Control (MAC) address, Internet Protocol (IP)address, or the like.

FIG. 3 is a block diagram of the information retrieval system 102 andRepository 104 of FIG. 1. As mentioned in relation to FIG. 1, therepository 104 is preferably contained within the information retrievalsystem 102. The information retrieval system 102 preferably includes thefollowing components: at least one data processor or central processingunit (CPU) 302; a memory 308; input and/or output devices 306, such as amonitor and keyboard; communications circuitry 304 for communicatingwith the network 110 (FIG. 1), creator device/s 106 (FIG. 1),contributor device/s 112 (FIG. 1), and/or searcher device 108 (FIG. 1);and at least one bus 310 that interconnects these components.

Memory 308 preferably includes an operating system 312, such as but notlimited to, VXWORKS™, LINUX™, or WINDOWS™ having instructions forprocessing, accessing, storing, or searching data, etc. Memory 308 alsopreferably includes communication procedures 314 for communicating withthe network 110 (FIG. 1), creator device/s 106 (FIG. 1), contributordevice/s 112 (FIG. 1), and/or searcher device 108 (FIG. 1); a collectionengine 316 for receiving and storing documents; a search engine 324;expertise adjustment procedures 326; a repository 104, as shown in FIG.1; and a cache 338 for temporarily storing data.

The collection engine 316 comprises a keyword extractor or parser 318that extracts text and/or keywords from any suitable document, such asan ASCII or XML file, Portable Document Format (PDF) file, wordprocessing file, or the like. The collection engine 316 also preferablycomprises a concept identifier 320. The concept identifier 320 is usedto extract the document's important concepts. The concept identifier maybe a semantic, synaptic, or linguistic engine, or the like. In apreferred embodiment the concept identifier 320 is a semantic engine,such as TEXTANALYST™ made by MEGAPUTER INTELLIGENCE™ Inc. Furthermore,the collection engine 316 also preferably comprises a metadata filter322 for filtering and/or refining the concept/s identified by theconcept identifier 320. Once the metadata filter 322 has filtered and/orrefined the concept, metadata about each document is stored in therepository 104. Further details of the processes performed by thecollection engine 316 are discussed in relation to FIG. 4. In additionto refined concepts, metadata includes any data, other than raw content,associated with a document.

The search engine 324 is any standard search engine, such as a keywordsearch engine, statistical search engine, semantic search engine,linguistic search engine, natural language search engine, or the like.In a preferred embodiment, the search engine 324 is a semantic searchengine.

The expertise adjustment procedures 326 are used to adjust an object'sintrinsic score to an adjusted score based on the expertise of thesearcher, creator/s, and/or contributors. The expertise adjustmentprocedures 326 are described in further detail below in relation to FIG.5.

A file collection 328(1)–(N) is created in the repository 104 for eachobject input into the system, such as a document or source. Each filecollection 328(1)–(N) preferably contains: metadata 330(1)–(N), such asassociations between keywords, concepts, or the like; content332(1)–(N), which is preferably ASCII or XML text or the content'soriginal format; and contributions 334(1)–(N), such as contributorcomments or the like. At a minimum, each file collection containscontent 332(1)–(N). The repository 104 also contains user profiles336(1)–(N) for each user, i.e., each searcher, creator, or contributor.Each user profile 336(1)–(N) includes associated user activity, such aswhich files a user has created, commented on, opened, printed, viewed,or the like, and links to various file collections 328(1)–(N)that theuser has created or contributed to. Further details of use of therepository 104 are discussed in relation to FIG. 5.

FIG. 4 is a flow chart of document collection according to an embodimentof the invention. A creator supplies an object, such as a document orsource, to the searching procedures 220 (FIG. 2) at step 402. To supplya document, the creator may for example, supply any type of data filethat contains text, such as an email, word processing document, textdocument, or the like. A document comes from a source of the document.Therefore, to supply a source, the creator may provide a link to adocument, such as by providing a URL to a Web-page on the Internet, orsupply a directory that contains multiple documents. In a preferredembodiment, the creator also supplies his or her unique user identifier224 (FIG. 2), and any other data, such as privacy settings, or the like.The unique user identifier may be supplied without the creator'sknowledge, such as by the creator device 106 (FIG. 1) automaticallysupplying its IP or MAC address.

The document, source, and/or other data is then sent to the informationretrieval system 102 (FIG. 1) by the communication procedures 218 (FIG.2). The information retrieval system 102 (FIG. 1) receives the document,source, and/or other data at step 403. When supplied with a document,the keyword extractor or parser 318 (FIG. 3) parses the document and/orsource into ASCII text at step 404, and thereafter extracts theimportant keywords at step 408. However, when supplied with a source,the keyword extractor or parser 318 (FIG. 3) firstly obtains thedocument/s from the source before parsing the important keywords intotext.

Extraction of important keywords is undertaken using any suitabletechnique. These keywords, document, source, and other data are thenstored at step 406 as in the repository 104 as part of a file collection328(1)–(N) (FIG. 3). Also, the unique user identifier is used toassociate or link each file collection 328(1)–(N) (FIG. 3) created witha particular creator. This link between the creator and the filecollection is stored in the creator's user profile 336(1)–(N) (FIG. 3).The user profile data can be updated by the user him/herself or morepreferably by a system administrator.

In a preferred embodiment, the concept identifier 320 (FIG. 3) thenidentifies the important concept/s from the extracted keywords at step410. Again, in a preferred embodiment, the metadata filter 322 (FIG. 3)then refines the concept at step 412. The refined concept is then storedin the repository 104 as part of the metadata 330(1)–(N) (FIG. 3) withina file collection 328(1)–(N) (FIG. 3).

At any time, contributors can supply their contributions, at step 416,such as additional comments, threads, or other activity to be associatedwith the file collection 328(1)–(N). These contributions are received bythe information retrieval engine at step 418 and stored in therepository at step 420, as contributions 334(1)–(N). Alternatively,contributions may be received and treated in the same manner as adocument/source, i.e., steps 403–414.

FIG. 5 is a flow chart of a process for information retrieval accordingto an embodiment of the invention. A searcher using a searcher device108 (FIG. 1) submits a search request to the information retrievalsystem 102 (FIG. 1), at step 502. Submittal of this search occurs usingsearching procedures 220 (FIG. 2) and communication procedures 218 (FIG.2) on the searcher device 108 (FIG. 1). The search request preferablycontains one or more search terms, and the unique user identifier 224(FIG. 2) of the searcher.

The search is preferably conducted to locate objects. Objects preferablyinclude: content objects, such as documents, comments, or folders;source objects; people objects, such as experts, peers, or workgroups;or the like. A search for documents returns a list of relevantdocuments, and a search for experts returns a list of experts withexpertise in the relevant field. A search for sources returns a list ofsources from where relevant documents were obtained. For example,multiple relevant documents may be stored within a particular directoryor website.

The search is received at step 504 by the information retrieval system102. (FIG. 1) using communications procedures 314 (FIG. 3). Theinformation retrieval system 102 (FIG. 1) then searches the repository104 for relevant objects at step 506. This search is undertaken by thesearch engine 324 (FIG. 3), at step 506, using any known or yet to bediscovered search techniques. In a preferred embodiment, the searchundertakes a semantic analysis of each file collection 328(1)–(N) storedin the repository 104.

The search engine 324 (FIG. 3) then locates relevant objects 328(1)–(N)at step 508 and calculates an intrinsic score at step 510 for eachlocated object. By “located object,” it is meant any part of a filecollection that is found to be relevant, including the content, source,metadata, etc. Calculation of the intrinsic score is based on known, oryet to be discovered techniques for calculating relevancy of locatedobjects based solely on the located objects themseves, the repositoryitself and the search terms. In its simplest form, such a searchcalculates the intrinsic score based on the number of times that asearch term appears in the content 332(1)–(N) (FIG. 3) of locatedobjects. However, in a preferred embodiment, this calculation is alsobased on a semantic analysis of the relationship between words in thecontent 332(1)–(N) (FIG. 3).

The intrinsic score is then adjusted to an adjusted score by theexpertise adjustment procedures 326, at step 512. This adjustment takesthe expertise of the creator/s, searcher, and/or contributor/s intoaccount, as described in further detail below.

Once the intrinsic score has been adjusted to an adjusted score, a listof the located objects is sorted at step 514. The list may be sorted byany field, such as by adjusted score, intrinsic score, source, mostrecently viewed, creator expertise, etc. The list, preferably containinga brief record for each located object, is then transmitted to thesearcher device 108 (FIG. 1) at step 516. Each record preferablycontains the located object's adjusted score, creator, title, etc. Thelist is then received by the searcher device at step 518 and displayedto the searcher at step 520. In an alternative embodiment, sorting ofthe list is performed by the searching procedures 220 (FIG. 2) on thesearcher device 108 (FIG. 1).

Preferred algorithms for adjusting the intrinsic score (step 512 of FIG.5) will now be described. It should be appreciated that these algorithmsare merely exemplary and in no way limit the invention other than asclaimed. Calculation of the adjusted score from the intrinsic score isdependent on the objects searched for, such as documents, comments,sources, experts, or peers.

Expertise Adjustment when Searching for Documents

Search term(s) entered by the searcher may or may not be extended toform a query. Such possible extensions, include but are not limited to,synonyms or stemming of search term(s). Once the intrinsic score hasbeen calculated according to step 510 above, the adjusted score(RS_(—)ADJ) for each located document is calculated as follows:$\begin{matrix}\begin{matrix}{{RS\_ ADJ} = {{{Intrinsic}\mspace{14mu}{Document}\mspace{14mu}{Score}} + {{Expertise}\mspace{14mu}{Adjustment}}}} \\{= {{IDS} + {EA}}}\end{matrix} & (1)\end{matrix}$where the Intrinsic Document Score (IDS) is a weighted average between aDocument Content Score (DCS) and a Comments Content Score (CCS).IDS=a*DCS+(1−a)*CCS  (2)with “a” being a number between 0 and 1 and determining the importanceof the content of a document relative to the content of its attachedcomments.

The DCS and CCS are calculated by any methodology or technique. Existingsearch engine algorithms may be used to fulfill this task. Also notethat the DCS and CCS are not influenced by the searcher that entered thequery. In this embodiment, the DCS and CCS can be any number between 2and 100. The Expertise Adjustment (EA) is calculated as follows:EA=DCE+CCE  (3)where DCE is the Document Creator Expertise adjustment and CCE is theComments Contributors Expertise adjustment. The DCE adjustment takesinto account all activity performed by a given user and is computed asfollows:DCE=R 1(DCS)*W 1(RS _(—) EXP _(—) ABS)  (4)where R1(DCS) determines the maximal amount of the expertise adjustment,or, in other words, the range for the alteration due to the expertise ofthe creator of the document. This depends on the level of the DCS. Therange function is given by: $\begin{matrix}{{{R1}({DCS})} = {20*\left( {1 - \frac{\left| {{DCS} - 50} \right|}{100}} \right)}} & (5)\end{matrix}$

Extreme intrinsic scores, i.e., scores near 2 or 100, are lessinfluenced than scores near the middle, i.e., scores near 50. Themaximum possible change in a score is 20 when DCS=50 and linearlydecreases to 10 when DCS=100 or 2.

W1(RS_(—)EXP_(—)ABS) determines what percentage of available rangeR1(DCS), positively or negatively, is considered for adjusting theintrinsic score. It is given by: $\begin{matrix}{{{W1}\left( {{RS\_ EXP}{\_ ABS}} \right)} = \frac{\begin{matrix}{{{RS\_ EXP}{\_ ABS}({Creator})} -} \\{{RS\_ EXP}{\_ ABS}({Searcher})}\end{matrix}}{100}} & (6)\end{matrix}$where RS-EXP-ABS denotes the absolute relevance score of a user, thatis, the user expertise, be it searcher expertise, creator expertise, orcontributor expertise. The calculation of RS-EXP-ABS occurs as follows:$\begin{matrix}\begin{matrix}{{{RS}\text{-}{EXP}\text{-}{ABS}} = {3*{F\left( {{User}\mspace{14mu}{contribution}} \right)}*}} \\{{G\left( {{Company}\mspace{14mu}{expertise}} \right)}*} \\{H\left( {{Query}\mspace{14mu}{specificity}} \right)}\end{matrix} & (7)\end{matrix}$where F (User contribution) accounts for the relevancy of allcontributions made by the user, considering all documents created, allcomments contributed, and the user's definition of his or her folderswithin the software. These folders (private or public) constitute theuser's personal taxonomy. G (Company expertise) accounts for the companyexpertise about the query, i.e., whether a few or most employees in acompany have produced something relevant to the query. H (Queryspecificity) accounts for the specificity of the query within therepository, i.e., whether many or just a few file collections werecreated.

In detail: $\begin{matrix}\begin{matrix}{{F\left( {{User}\mspace{14mu}{{cont}.}} \right)} = {{\sum\limits_{\underset{documents}{i:{allrelevant}}}\left( {2*\left( {W_{i,\max} + C_{i}} \right)*\left( \frac{({DCS})_{i}}{100} \right)^{2}} \right)} +}} \\{{\sum\limits_{\underset{documents}{i:{allnonrelevant}}}C_{i}} + {2*{Taxonomy}}}\end{matrix} & (8)\end{matrix}$where the first sum is over all relevant documents and the second sum isover all non-relevant documents that possessed a relevant comment, i.e.,the comment was relevant but not the document. (DCS)_(i) is theIntrinsic document relevancy score attained for the i-th relevantdocument. Also, W_(i,max), is the user activity measure. C_(i) iscalculated as follows: $\begin{matrix}{C_{i} = {0.1*\left( {1 - {{Exp}\left( {- \frac{\#\mspace{14mu}{relevant}\mspace{14mu}{comments}\mspace{14mu}{in}\mspace{14mu}{Doc}_{i}\mspace{14mu}{by}\mspace{14mu}{this}\mspace{14mu}{user}}{2}} \right)}} \right)}} & (9)\end{matrix}$and is the reward assigned to matching comments made on documents,relevant or not. A matched comment is not necessarily attached to arelevant document.

W_(i,max) accounts for the type of contribution (such as but not limitedto creation, commenting, or highlighting). In short, W_(i,max) is themaximum of the following weights (if applicable).

-   -   W_(i,edit)=1 if the user created or edited i-th file collection,    -   W_(i,comment)=0.5*Max*(0.7−Min_(comments)*(Level))/6 if the user        commented on the i-th file collection. Since these comments are        organized in a threaded discussion, the weight will also depend        on how remote a comment is to the file collection itself. For        example, a comment on a comment on a comment to the original        file collection will receive a lesser weight than a comment on        the original file collection. In the formula, Level measures how        remote the comment is from the file collection. The least remote        comment is taken into consideration as long as it is closer than        six comments away from the parent file collection.    -   W_(i,rename)=0.8 if the user renamed i-th file collection.    -   W_(i,highlight)=0.8 if the user highlighted some subparts of        i-th file collection.    -   W_(i,link)=0.8 if the user linked the file collection to another        file collection or “external” URL.        ${Taxonomy} = \left\{ \begin{matrix}        1 & {{If}\mspace{14mu}{Query}\mspace{14mu}{term}\mspace{14mu}{found}\mspace{14mu}{within}\mspace{14mu}{{user}'}s\mspace{14mu}{taxonomy}} \\        0 & {Otherwise}        \end{matrix} \right.$

The taxonomy in this preferred embodiment stands for folder names. Eachuser has built some part of the repository by naming folders,directories, or sub-directories. For example, creator 1 might havegrouped his Hubble telescope pictures in a folder called “Space Images.”Then term “Space Images” becomes part of the user's taxonomy.

Within an organization or enterprise, some of the taxonomy (folderstructure) has been defined by the organization or enterprise itself andhas “no owners.” In this case, each folder has an administrator whobestows rights to users, such as the right to access the folder, theright to edit any documents within it, the right to edit only documentsthat the specific user created, or the right to view but not edit orcontribute any document to the folder. Only the names of the foldersthat a user creates are part of his or her taxonomy. $\begin{matrix}{{{G\left( {{Company}\mspace{14mu}{expertise}} \right)} = {{1 + {{Log}\left( \frac{P}{E} \right)}} = {IEF}}},} & (10)\end{matrix}$where Log is the logarithmic function base 10; P is the total number ofusers; and E is the number of relevant experts. The number of relevantexperts is calculated by determining how many unique creators andcontributors either created or contributed to the located documents. IEFstands for Inverse Expertise Frequency.

This adjustment raises the adjusted scores when there are few relevantexperts within the company. $\begin{matrix}\begin{matrix}{{H\left( {{Query}\mspace{14mu}{specificity}} \right)} = {1 + {\frac{1}{{Log}({NCO})}\mspace{11mu}{Log}\;\left( \frac{NCO}{NCOR} \right)}}} \\{= {{2 - \frac{{Log}({NCOR})}{{Log}({NCO})}} = {IWCOF}}}\end{matrix} & (11)\end{matrix}$where Log is the logarithmic function base 10; NCO is the total numberof content objects available in the database at the time of the query;and NCOR is the total number of relevant content objects for a givenquery. IWCOF stands for the Inverse Weighted Content Objects Frequency.Preferably, in this embodiment, NCO, NCOR and IWCOF are only calculatedusing non-confidential content objects.

IWCOF is similar to IEF as it adjusts the score by slightly raising theadjusted score when only a few relevant content objects are found in thedatabase. Therefore, the absolute relevance score for a given user (orthe user expertise) is: $\begin{matrix}\begin{matrix}{{{RS}\text{-}{EXP}\text{-}{ABS}} = {3*\left( {1 + {{Log}\left( \frac{P}{E} \right)}} \right)\left( {2 - \frac{{Log}({NCOR})}{{Log}({NCO})}} \right)*}} \\{\left( {{\sum\limits_{\underset{documents}{i:{allrelevant}}}\left( {2*\left( {\underset{i,\max}{w} + C_{i}} \right)*\left( \frac{({DCS})_{i}}{100} \right)^{2}} \right)} +} \right.} \\\left. {{\sum\limits_{\underset{documents}{i:{allnonrelevant}}}C_{i}} + {2*{Taxonomy}}} \right) \\{= {3*{IEF}*{IWCOF}*}} \\{\left( {{\sum\limits_{\underset{documents}{i:{allrelevant}}}\left( {2*\left( {\underset{i,\max}{w} + C_{i}} \right)*\left( \frac{({DCS})_{i}}{100} \right)^{2}} \right)} +} \right.} \\\left. {{\sum\limits_{\underset{documents}{i:{allnonrelevant}}}C_{i}} + {2*{Taxonomy}}} \right)\end{matrix} & (12)\end{matrix}$

Using the above equations, the intrinsic score is increased to anadjusted score if the creator of the content objects is moreknowledgeable about the searched subject matter than the person thatentered the query, ie., if the creator expertise is higher than thesearcher expertise. On the other hand, the intrinsic score is decreasedto an adjusted score if the creator is less knowledgeable about thesearched subject matter than the searcher, i.e., if the creatorexpertise is lower than the searcher expertise.

To calculate the Comments Contributors Expertise Adjustment (CCE) thefollowing equation is used: $\begin{matrix}{{CCE} = {5*\left( {{2*\frac{{Exp}({Dx})}{1 + {{Exp}({Dx})}}} - 1} \right)}} & (13)\end{matrix}$where $\begin{matrix}{{\Delta\; x} = {\frac{1}{50}{\sum\limits_{DistinctContributors}\left( {{{RS\_ EXP}{\_ ABS}({Contributors})} - {{RS\_ EXP}{\_ ABS}({Searcher})}} \right)}}} & (14)\end{matrix}$

Once these adjustments have been computed, one has to ensure that therelevancy score from (1) is in the appropriate range and that it ispreferably in this embodiment an integer. This is obtained as follows:RS _(—) ADJ=Min(100, Max (1, Round(RS _(—) ADJ)))  (15)where Round(d) rounds the number d to its nearest integer.

Expertise Adjustment when Searching for Sources

Once the intrinsic score has been calculated according to step 510above, the adjusted score for sources (RSS_(—)ADJ) for each source iscalculated as follows: $\begin{matrix}\begin{matrix}{{RSS\_ ADJ} = {{{intrinsic}\mspace{14mu}{Source}\mspace{14mu}{Content}\mspace{14mu}{score}} + {{expertise}\mspace{14mu}{adjustment}}}} \\{= {{SCS} + {{{R2}({SCS})}*{{W2}\left( {{RS\_ EXP}{\_ ABS}} \right)}}}}\end{matrix} & (16)\end{matrix}$where SCS is the intrinsic Source Content Score computed, which is,preferably in this embodiment, defined here as the maximum of all theintrinsic Document Content Scores (DCS) that were created from eachsource, i.e.,SCS=MAX(DCS)  (17)

For example, multiple documents may have been saved as multiple filecollections from a single Web-site.

R2(SCS) determines the maximal amount of the expertise adjustment, or,in other words, the range for the alteration due to the expertise of thecreator of the document taken from the source, which depends on thelevel of the intrinsic source score, i.e., SCS. The range function isgiven by: $\begin{matrix}{{{R1}({SCS})} = {20*\left( {1 - \frac{\left| {{SCS} - 50} \right|}{100}} \right)}} & (18)\end{matrix}$

Extreme scores are less influenced than scores in the middle. Themaximum possible change in a score is 20 when SCS=50 and linearlydecreases to 10 when SCS=100 or 2.

W2(RS_(—)EXP_(—)ABS) determines what percentage of available range forthe expertise adjustment, R2(SCS), positively or negatively, isconsidered for building the scoring. It is given by: $\begin{matrix}{{{W2}\left( {{RS\_ EXP}{\_ ABS}} \right)} = \frac{\begin{matrix}{{{MAX}\left( {{RS\_ EXP}{\_ ABS}({Creator})} \right)} -} \\{{RS\_ EXP}{\_ ABS}({Searcher})}\end{matrix}}{100}} & (19)\end{matrix}$where RS_(—)EXP_(—)ABS is the absolute relevance score of the expert (asdefined previously). MAX(RS_(—)EXP_(—)ABS(Creator)) is the maximum ofabsolute expertise scores over all creators that have created filecollections from this source. RS_(—)EXP_(—)ABS(Searcher) is the absoluterelevance score of the searcher. In other words, the intrinsic score forthe source is adjusted upward to an adjusted score if the maximumcreator expertise of all creators for a particular source exceeds thesearcher expertise. On the other hand, the intrinsic score for thesource is lowered to an adjusted score if the creator expertise of allcreators for a particular source is lower than the searcher expertise.

Once this adjustment has been computed, one has to ensure that therelevancy score is in the appropriate range and that it is preferably inthis embodiment an integer. This is obtained as follows:RSS _(—) ADJ=Min(100, Max (1, Round(RSS _(—) ADJ)))  (20)where Round(d) rounds the number d to its nearest integer.

In this way, the adjusted score for each document (RS_(—)ADJ) or theadjusted score for sources (RSS_(—)ADJ) is calculated based on theexpertise of the searcher, creator/s, and/or contributor/s. Suchadjusted scores provide a significant improvement in precision over thatattainable through conventional information retrieval systems.

Expertise Adjustment when Searching for Peers

When users are looking for peers rather than experts an adjustedrelevancy score is calculated. Peers are other users that have a similarexpertise or come from a similar, or the same, department as thesearcher. The adjusted relevancy score uses the expertise values andadjusts them with respect to the searcher's expertise. This is thesimilar to resorting the list with respect to the searcher, but insteadrecalculates the values themselves.

Once the expertise for each user has been determined, they are adjustedwith respect to the searcher expertise. The adjusted relative orpersonalized relevancy score for an expert is defined by:Adjusted Rel=100−10*|(√{square root over (RS-EXP-ABS)}−√{square rootover (RS-EXP-ABS)}_(searcher) +10)|  (21)

The adjusted relevancy score is a measure of the difference between twolevels of expertise. The square root maps the difference to a continuousand monotone measure while diminishing the importance of differenceswhen two experts are far apart. It is also asymmetric in the sense thatit favors expertise above the searcher expertise. Finally, recall that|K| represents the absolute value of K (i.e., the difference).

An example of a method for personalizing information retrieval using theabove formulae will now be described. It should, however, be appreciatedthat this example is described herein merely for ease of explanation,and in no way limits the invention to the scenario described. Table 1sets out the environment in which a search is conducted. Furthermore, inthis illustration, the factor a (from formula 2, determining theimportance of the content of a document relative to its attachedcomments) has been arbitrarily set to 1.

TABLE 1 Number of users # experts 100 10 Total Number of File # ofrelevant File Collections Collections # of relevant comments 1000 10 10Departments of experts Names Marketing Adam M. Bryan M. Christie M.David M. Engineering Eric E. Fred E. Gail E. Finance Hugo F. Henry F.Legal Ivan L. Contributors (total # of contributions, # of relevant FileCollection number Creator contributions)  11 Adam M. Bryan M. (2, 2)Christie M. (1, 0) 101 Adam M. 201 David M. David M. (2) Hugo F. (3) 301David M. David M. (1) 401 Christie M. Adam M. (1) Christie M. (3, 1)David M. (1) Eric E. (2) Fred E. (2, 2) Hugo F. (3) Ivan L. (5) 501 GailE. Eric E. (1, 0) Fred E. (5, 0) Gail E. (4, 0) 601 Eric E. 701 Henry F.Henry F. (6, 0) Hugo F. (7, 1) Bryan M. (1, 1) 801 Hugo F. 901 Ivan L.Henry F. (1, 0) 999 John I. Bryan M. (2, 2) Fred E. (3, 1) Attachedcomments intrinsic File Collection Intrinsic score score, by author FileCollection number DCS score CCS scores  11 85 Bryan M., 1 Bryan M., 1Christie M., 0 101 85 201 100 David M., 0 Hugo F. 0 301 50 David M., 0401 75 Adam M., 0 Christie M., 1 David M., 0 Eric E., 0 Fred E., 1 FredE. 1 Hugo F., 0 Ivan L., 0 501 80 Eric E., 0 Fred E., 0 Gail E., 0 60180 701 40 Henry F., 0 Hugo F., 1 Hugo F., 0 Bryan M., 1 801 60 901 70Henry F., 0 999 0 Byran M., 1 Bryan M., 1 Fred E., 1 Fred E., 0 Taxonomymatches Christie M. Bryan M. File Collection number Original source  11cnn.com The source name here is 101 nytimes.com truncated to the “rootlevel” for 201 microsoft.com simplification purposes. In reality 301bbc.com it is the entire url tag. For example, 401 nytimes.comhttp://www.cnn.com/2002/WORLD/ 501 cnn.commeast/03/26/arab.league/index.html 601 nytimes.com 701 latimes.com 801bbc.com 901 corporate intranet

For this example, 100 users having a total number of 1000 filecollections in the repository yields 10 experts and 10 relevant filecollections. There are also 10 comments that are found to be relevant.The enterprise in which the example takes place has four departments,namely marketing, engineering, finance, and legal. For ease ofexplanation, each employee's last name begins with the department inwhich they work.

Once the repository 104 (FIG. 1) has been searched (step 506—FIG. 5) andall relevant documents located (step 508—FIG. 5), an Intrinsic DocumentScore (IDS) is calculated for each located document. This score is aweighted average between a Document Content Score (DCS) and a CommentContent Score (CCS). The DCS and CCS are calculated using any standardsearch engine techniques. CCS is the Comment Content Score calculated byany means such as semantic engine, frequency of words, etc.

Using formulae 7–12 above, the expertise of each searcher, creator,and/or contributor is then calculated. The calculations for F(Usercontribution) yield the results in Table 2 below.

TABLE 2 F(user contribution) Second File W_(i) by File C_(i) by FileFirst sum in formula sum in Taxonomy User Collection collectioncollection Details Value formula match Adam M. 11 1 0 2 * 1 *(85/100){circumflex over ( )}2 1.445 0 101 1 0 2 * 1 *(85/100){circumflex over ( )}2 1.445 0 0 F(Adam M.) 2.89 Bryan M. 11 0.50.063 2 * (0.5 + 0.063) * (.85){circumflex over ( )}2 0.814 0 701 0.50.039 2 * (0.5 + 0.039) * (.4){circumflex over ( )}2 0.172 0 999 0.50.063 2 * (0.5 + 0.063) * 0 0 0.063 2 F(Bryan M.) 3.049 Christie M. 4011 0.039 2 * (1 + 0.039) * (75/100){circumflex over ( )}2 1.169 0.039 2F(Christie M.) 3.208 David M. 201 1 0 2 * 1 * 1 2 0 301 1 0 2 * 1 *(0.5){circumflex over ( )}2 0.5 0 0 F(David M.) 2.5 Eric E. 601 1 0 2 *1 * (.8){circumflex over ( )}2 1.28 0 0 F(Eric E.) 1.28 Fred E. 401 0.50.063 2 * (0.5 + 0.063) * .75{circumflex over ( )}2 0.633 999 0.5 0.0392 * (0.5 + 0.039) * 0 0 0.039 0 F(Fred E.) 0.672 Gail E. 501 1 0 2 * 1 *.8{circumflex over ( )}2 1.28 0 0 F(Gail E.) 1.28 Hugo F. 801 1 0 2 *1 * .6{circumflex over ( )}2 0.72 0 0 F(Hugo F.) 0.72 Ivan L. 901 1 02 * 1 * .7{circumflex over ( )}2 0.98 0 0 F(Ivan L.) 0.98

Using formulae 10 and 11, G(Company Expertise) is calculated to be 2while H(Query Specificity) is calculated to be 1.667. These values andthe values in Table 2 are plugged into formula 7 to arrive at thefollowing expertise values:

TABLE 3 Name RS-EXP-ABS Adam M. 29 Bryan M. 30 Christie M. 32 David M.25 Eric E. 13 Fred E. 7 Gail E. 13 Hugo F. 7 Henry F. 3 Ivan L. 10

W1(RS_(—)EXP_(—)ABS) is then calculated using formula 6 (for differentsearcher expertises) to yield the following results:

TABLE 4 W(RS_(—)EXP_(—)ABS) Searcher Expertise Name 0 5 10 15 20 25 3035 40 45 Adam M. 0.29 0.24 0.19 0.14 0.09 0.04 −0.01 −0.06 −0.11 −0.16Bryan M. 0.3 0.25 0.2 0.15 0.1 0.05 0 −0.05 −0.1 −0.15 Christie M. 0.320.27 0.22 0.17 0.12 0.07 0.02 −0.03 −0.08 −0.13 David M. 0.25 0.2 0.150.1 0.05 0 −0.05 −0.1 −0.15 −0.2 Eric E. 0.13 0.08 0.03 −0.02 −0.07−0.12 −0.17 −0.22 −0.27 −0.32 Fred E. 0.07 0.02 −0.03 −0.08 −0.13 −0.18−0.23 −0.28 −0.33 −0.38 Gail E. 0.13 0.08 0.03 −0.02 −0.07 −0.12 −0.17−0.22 −0.27 −0.32 Hugo F. 0.07 0.02 −0.03 −0.08 −0.13 −0.18 −0.23 −0.28−0.33 −0.38 Henry F. 0.03 −0.02 −0.07 −0.12 −0.17 −0.22 −0.27 −0.32−0.37 −0.42 Ivan L. 0.1 0.05 0 −0.05 −0.1 −0.15 −0.2 −0.25 −0.3 −0.35

DCE and CCE are then calculated using formulae 4, 5, 13, and 14 (fordifferent searcher expertises) to yield the following results:

TABLE 5 DCE Calculations File collection ID R1 W(searcher exp = 0) DCE(0)  11 13 0.29 3.77 101 13 0.29 3.77 201 10 0.25 2.5 301 20 0.25 5 40115 0.32 4.8 501 14 0.13 1.82 601 14 0.13 1.82 701 18 0.03 0.54 801 180.07 1.26 901 16 0.1 1.6 File collection ID R1 W(searcher exp = 30) DCE(30)  11 13 −0.01 −0.13 101 13 −0.01 −0.13 201 10 −0.05 −0.5 301 20−0.05 −1 401 15 0.02 0.3 501 14 −0.17 −2.38 601 14 −0.17 −2.38 701 18−0.27 −4.86 801 18 −0.23 −4.14 901 16 −0.2 −3.2 CCE Calculations Filecollection “Delta X” or Dx ID Searcher Exp = 0 Searcher Exp = 30 CCE (0)CCE(30)  11 1.24 0.04 2.76 0.1 101 0 0 0 0 201 0.64 −0.56 1.55 −1.36 3010.5 −0.1 1.22 −0.25 401 2.46 −1.74 4.21 −3.51 501 0.66 −1.14 1.59 −2.58601 0 0 0 0 701 0.8 −1 1.9 −2.31 801 0 0 0 0 901 0.06 −0.54 0.15 −1.32

The Expertise Adjustment (EA) is then calculated according to formula 3to yield the following results for EA:

TABLE 6 Expertise Adjustment (EA) Values for DCE and CCE are from Table5 above File collection ID Searcher expertise = 0 Searcher expertise =30  11 6.53 −0.03 101 3.77 −0.13 201 4.05 −1.86 301 6.22 −1.25 401 9.01−3.21 501 3.41 −4.96 601 1.82 −2.38 701 2.44 −7.17 801 1.26 −4.14 9011.75 −4.52 This entry is DCE + This entry is DCE + CCE CCE when thesearcher when the searcher expertise is 0 expertise is 30

Finally, the adjusted score (RS_(—)ADJ) for each located document iscalculated using formula 1 to yield the following results:

TABLE 7 RS_(—)ADJ RS_(—)ADJ Document Adjusted score Adjusted score Filecollection ID Intrinsic score Searcher exp = 0 Searcher exp = 30  11 8592 85 101 85 89 85 201 100 100 98 301 50 56 49 401 75 84 72 501 80 83 75601 80 82 78 701 40 42 33 801 60 61 56 901 70 72 65

In a similar manner, the adjusted scores are calculated when searchingfor sources as per tables 8–12 below.

TABLE 8 File collection created Creators Source name from source of Filecollections cnn.com  11, 501 Adam M., Gail E. microsoft.com 201 David M.nytimes.com 101, 401, 601 Adam M., Christie M., Eric E. bbc.com 801 HugoF. latimes.com 701 Henry F. corporate intranet 901 Ivan L.

TABLE 9 SCS calculations Source SCS cnn.com 85 microsoft.com 100nytimes.com 85 bbc.com 60 latimes.com 40 corporate intranet 70

TABLE 10 R2 calculations cnn.com 13 microsoft.com 10 nytimes.com 13bbc.com 18 latimes.com 18 corporate intranet 16

TABLE 11 W2 Calculations Searcher Searcher Expertise = 0 Expertise = 30cnn.com 0.29 −0.01 microsoft.com 0.25 −0.05 nytimes.com 0.32 0.02bbc.com 0.07 −0.23 latimes.com 0.03 −0.27 corporate intranet 0.1 −0.2

TABLE 12 Adjusted relevancy scores RSS_(—)ADJ RSS_(—)ADJ SCS SearcherSearcher Intrinsic Source name Expertise = 0 Expertise = 30 scorecnn.com 89 85 85 microsoft.com 100 100 100 nytimes.com 89 85 85 bbc.com61 56 60 latimes.com 41 35 40 corporate intranet 72 67 70

As can be seen the intrinsic scores of each document and/or source isadjusted to an adjusted score based on the expertise of the users. Inother words, a document and/or source that may have been less relevant,is adjusted so that it is more relevant, or visa versa. In this way, theprecision of document and/or source relevancy is improved.

While the foregoing description and drawings represent preferredembodiments of the present invention, it will be understood that variousadditions, modifications and substitutions may be made therein withoutdeparting from the spirit and scope of the present invention as definedin the accompanying claims. In particular, it will be clear to thoseskilled in the art that the present invention may be embodied in otherspecific forms, structures, arrangements, proportions, and with otherelements, materials, and components, without departing from the spiritor essential characteristics thereof. The presently disclosedembodiments are therefore to be considered in all respects asillustrative and not restrictive, the scope of the invention beingindicated by the appended claims, and not limited to the foregoingdescription. Furthermore, it should be noted that the order in which theprocess is performed may vary without substantially altering the outcomeof the process.

1. A computer implemented method for personalizing informationretrieval, comprising: receiving at an information retrieval system asearch request from a searcher, where said search request contains atleast one search term; searching a plurality of objects based on said atleast one search term; finding at least one located object from saidplurality of objects, where said at least one located object isassociated with said at least one search term; calculating an intrinsicscore for said at least one located object, where said intrinsic scoreis based on said at least one search term; adjusting said intrinsicscore to an adjusted score based on at least one of a creator expertiseof a creator of said at least one located object, a contributorexpertise of a contributor to said at least one located object, and asearcher expertise of said searcher; and transmitting a list to saidsearcher, where said list comprises said at least one located object andsaid adjusted score for said at least one located object.
 2. The methodof claim 1, wherein said search request identifies said searcher througha unique user identifier.
 3. The method of claim 2, wherein saidplurality of objects include content objects, source objects, or peopleobjects.
 4. The method of claim 5, wherein said adjusting furthercomprises lowering said intrinsic score to said adjusted score for eachlocated content object or source object having a creator expertise thatis lower than said searcher expertise.
 5. The method of claim 1, whereinsaid at least one located object includes multiple objects selected froma group consisting of: content objects, source objects, and peopleobjects.
 6. The method of claim 5, wherein said adjusting furthercomprises raising said intrinsic score to said adjusted score for eachlocated content or source object having a creator expertise higher thansaid searcher expertise.
 7. The method of claim 5, wherein saidadjusting further comprises: locating objects associated with saidsearcher using said unique user identifier; ascertaining said searcherexpertise based on said at least one search term and said objectsassociated with said searcher; determining said creator expertise andsaid contributor expertise for each located object; raising saidintrinsic score to said adjusted score for each located content objector source object having a creator expertise higher than said searcherexpertise; lowering said intrinsic score to said adjusted score for eachlocated content object or source object having a creator expertise thatis lower than said searcher expertise; and changing said intrinsic scoreto said adjusted score for each located people object according to saidsearcher and creator expertise.
 8. The method of claim 5, wherein saidadjusting comprises: ascertaining said searcher expertise based on saidsearch terms and any objects associated with said searcher; determiningat least one of said creator expertise and said contributor expertisefor each located content or source object; raising said intrinsic scoreto said adjusted score for each located content or source object havinga creator expertise higher than said searcher expertise; lowering saidintrinsic score to said adjusted score for each located object having acreator expertise that is lower than said searcher expertise; andchanging said intrinsic score to said adjusted score for each locatedpeople object according to said searcher and creator expertise.
 9. Themethod of claim 5, wherein said searcher expertise is dependent on thesum of the intrinsic scores of all located objects created by saidsearcher.
 10. The method of claim 1, wherein said searching isundertaken using a search technique selected from a group consisting of:semantic processing, syntactic processing, natural language processing,statistical processing, and any combination of the aforementionedtechniques.
 11. The method of claim 1, wherein said searcher expertiseis dependent on the sum of all non-relevant objects of said plurality ofobjects not created by said searcher but containing relevant comments ofsaid searcher.
 12. The method of claim 1, wherein said searcherexpertise is dependent on the sum of all non-relevant objects of saidplurality of objects created by said searcher but containing relevantcomments of said searcher.
 13. The method of claim 1, wherein saidsearcher expertise is dependent on the total number of users relative tothe total number of relevant users.
 14. The method of claim 1, whereinsaid searcher expertise is dependent on the total number of objects ofsaid plurality of objects relative to the total number of relevantobjects of said plurality of objects.
 15. The method of claim 1, whereinsaid searcher expertise is dependent on the searcher's taxonomy.
 16. Themethod of claim 1, wherein said searcher expertise is dependent onwhether the searcher created, edited, commented on, renamed,highlighted, linked, printed, copied or any activity that is monitoredin a log file, said at least one located object.
 17. The method of claim1, wherein said creator expertise is dependent on the sum of theintrinsic scores of all located objects created by said creator.
 18. Themethod of claim 1, wherein said creator expertise is dependent on thesum of all non-relevant objects of said plurality of objects not createdby said creator but containing relevant comments of said creator. 19.The method of claim 1, wherein said creator expertise is dependent onthe sum of all non-relevant objects of said plurality of objects createdby said creator but containing relevant comments of said creator. 20.The method of claim 1, wherein said creator expertise is dependent onthe total number of users relative to the total number of relevantusers.
 21. The method of claim 1, wherein said creator expertise isdependent on the total number of objects of said plurality of objectsrelative to the total number of relevant objects of said plurality ofobjects.
 22. The method of claim 1, wherein said creator expertise isdependent on a creator's taxonomy.
 23. The method of claim 1, whereinsaid creator expertise is dependent on whether the creator created,edited, commented on, renamed, highlighted, or linked, printed, copiedor any activity that is monitored in a log file, said at least onelocated object.
 24. The method of claim 1, wherein said contributorexpertise is dependent on the sum of the intrinsic scores of all locatedobjects created by said contributor.
 25. The method of claim 1, whereinsaid contributor expertise is dependent on the sum of all non-relevantobjects of said plurality of objects not created by said contributor butcontaining relevant comments of said contributor.
 26. The method ofclaim 1, wherein said contributor expertise is dependent on the sum ofall non-relevant objects of said plurality of objects created by saidcontributor but containing relevant comments of said contributor. 27.The method of claim 1, wherein said contributor expertise is dependenton the total number of users relative to the total number of relevantusers.
 28. The method of claim 1, wherein said contributor expertise isdependent on the total number of objects of said plurality of objectsrelative to the total number of relevant objects of said plurality ofobjects in the repository.
 29. The method of claim 1, wherein saidcontributor expertise is dependent on the contributor's taxonomy. 30.The method of claim 1, wherein said contributor expertise is dependenton the whether the contributor created, edited, commented on, renamed,highlighted, linked, printed, copied or any activity that is monitoredin a log file, said at least one located object.
 31. The method of claim1, further comprising, prior to said transmitting, sorting said listbased on adjusted score.
 32. The method of claim 1, wherein saidtransmitting comprises sending a list of people objects and theirassociated adjusted scores to said searcher.
 33. The method of claim 1,wherein said transmitting comprises sending a list of source objects andtheir associated adjusted scores to said searcher.
 34. A computerprogram product for personalizing information retrieval, the computerprogram product comprising a computer readable storage and a computerprogram stored therein, the computer program comprising: instructionsfor receiving at an information retrieval system a search request from asearcher, where said search request contains at least one search term;instructions for searching a plurality of objects based on said at leastone search term; instructions for finding at least one located objectfrom said plurality of objects, where said at least one located objectis associated with said at least one search term; instructions forcalculating an intrinsic score for said at least one located object,where said intrinsic score is based on said at least one search term;instructions for adjusting said intrinsic score to an adjusted scorebased on at least one of a creator expertise of a creator of said atleast one located object, a contributor expertise of a contributor tosaid at least one located object, and a searcher expertise of saidsearcher; and instructions for transmitting a list to said searcher,where said list comprises said at least one located object and saidadjusted score.
 35. The computer program product of claim 34, whereinsaid computer program further comprises: instructions for ascertainingsaid searcher expertise based on said search terms and any objectsassociated with said searcher; instructions for determining said atleast one of said creator expertise and said contributor expertise forsaid at least one located object; instructions for raising saidintrinsic score to said adjusted score for each located content orsource object having a creator expertise higher than said searcherexpertise; and instructions for lowering said intrinsic score to saidadjusted score for each located content or source object having acreator expertise that is lower than said searcher expertise.
 36. Asystem for personalizing information retrieval comprising: at least onesearcher device, creator device, and contributor device coupled to anetwork; a repository containing one or more objects; and an informationretrieval system comprising: a Central Processing Unit (CPU); and amemory comprising: instructions for receiving at an informationretrieval system a search request from a searcher, where said searchrequest contains at least one search term; instructions for searching aplurality of objects based on said at least one search term;instructions for finding at least one located object from said pluralityof objects, where said at least one located object is associated withsaid at least one search term; instructions for calculating an intrinsicscore for said at least one located object, where said intrinsic scoreis based on said at least one search term; instructions for adjustingsaid intrinsic score to an adjusted score based on at least one of acreator expertise of a creator of said at least one located object, acontributor expertise of a contributor to said at least one locatedobject, and a searcher expertise of said searcher; and instructions fortransmitting a list to said searcher, where said list comprises said atleast one located object and said adjusted score.
 37. The system ofclaim 36, wherein said memory further comprises: instructions forascertaining said searcher expertise based on said search terms and anyobjects associated with said searcher; instructions for determining saidat least one of said creator expertise and said contributor expertisefor said at least one located object; instructions for raising saidintrinsic score to said adjusted score for each located content orsource object having a creator expertise higher than said searcherexpertise; and instructions for lowering said intrinsic score to saidadjusted score for each located content or source object having acreator expertise that is lower than said searcher expertise.